Abstract
Early de-oxygenation (initial dip) is an indicator of the primal cortical activity source in functional neuro-imaging. In this study, initial dip's existence and its estimation in relation to the differential pathlength factor (DPF) and data drift were investigated in detail. An efficient algorithm for estimation of drift in fNIRS data is proposed. The results favor the shifting of the fNIRS signal to a transformed coordinate system to infer correct information. Additionally, in this study, the effect of the DPF on initial dip was comprehensively analyzed. Four different cases of initial dip existence were treated, and the resultant characteristics of the hemodynamic response function (HRF) for DPF variation corresponding to particular near-infrared (NIR) wavelengths were summarized. A unique neuro-activation model and its iterative optimization solution that can estimate drift in fNIRS data and determine the best possible fit of HRF with free parameters were developed and herein proposed. The results were verified on simulated data sets. The algorithm is applied to free available datasets in addition to six healthy subjects those were experimented using fNIRS and observations and analysis regarding shape of HRF were summarized as well. A comparison with standard GLM is also discussed and effects of activity strength parameters have also been analyzed.
Highlights
Near-infrared spectroscopy (NIRS) is an emerging non-invasive neuro-imaging methodology that measures the cortical activity based on blood chromophores (Noori et al, 2017; Khan et al, 2018)
The simultaneous recording from multiple locations on surface of scalp could result in improved accuracy and reliability. Functional nearinfrared spectroscopy (fNIRS) has several advantages over other currently existing neuro-imaging modalities, which measure hemodynamic response function (HRF) characteristics
The ability to measure the spectroscopic information with NIRS allows one to characterize changes in HbO and HbR separately which results in less ambiguous analysis of activity induced volume and metabolic changes than Blood Oxygen Level Dependent (BOLD) in fMRI (Jasdzewski et al, 2003)
Summary
Near-infrared spectroscopy (NIRS) is an emerging non-invasive neuro-imaging methodology that measures the cortical activity based on blood chromophores (Noori et al, 2017; Khan et al, 2018). It is well-known fact that regional blood flow and neural activities are tightly coupled in time and space (Lindauer et al, 2001; Salvador et al, 2010; Whiteman et al, 2017). FNIRS has several advantages over other currently existing neuro-imaging modalities, which measure hemodynamic response function (HRF) characteristics Those includes reasonable spatial resolution and high temporal
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